Empirical Economics

, Volume 47, Issue 1, pp 227–251 | Cite as

TFP growth and its determinants: a model averaging approach

  • Michael Danquah
  • Enrique Moral-Benito
  • Bazoumana Ouattara


Total Factor Productivity (TFP) accounts for a sizable proportion of the income differences across countries. Two challenges remain to researchers aiming to explain these differences: on the one hand, TFP growth is hard to measure empirically; on the other hand, model uncertainty hampers consensus on its key determinants. This paper combines a non-parametric measure of TFP growth with Bayesian model averaging techniques in order to address both issues. Our empirical findings suggest that the most robust TFP growth determinants are time-invariant unobserved heterogeneity and trade openness. We also investigate the main determinants of two TFP components: efficiency change (i.e., catching up) and technological progress.


Bayesian model averaging Productivity Nonparametric methods 

JEL Codes

O47 C11 C14 C23 


  1. Abramovitz M (1956) Resources and output trends in the United States since 1870. Am Econ Rev 46:5–23Google Scholar
  2. Acemoglu D (2010) When does labor scarcity encourage innovation? J Polit Econ 118:1037–1078CrossRefGoogle Scholar
  3. Acemoglu D, Johnson S, Robinson J (2001) The colonial origins of comparative development: an empirical investigation. Am Econ Rev 91:1369–1401CrossRefGoogle Scholar
  4. Badunenko O, Henderson D, Zelenyuk V (2008) Technological change and transition: relative contributions to worldwide growth during the 1990s. Oxf Bull Econ Stat 70:461–492CrossRefGoogle Scholar
  5. Barro R (1991) Economic growth in a cross section of countries. Q J Econ 106:407–443CrossRefGoogle Scholar
  6. Beaudry P, Green D (2002) Population growth, technological adoption, and economic outcomes in the information era. Rev Econ Dyn 5:749–774CrossRefGoogle Scholar
  7. Benhabib J, Spiegel M (1994) The role of human capital in economic development: evidence from aggregate cross-country data. J Monet Econ 34:143–174CrossRefGoogle Scholar
  8. Benhabib J, Spiegel M (2005) Human capital and technology diffusion. In: Aghion P, Durlauf S (eds) Handbook of economic growth, vol 4. North Holland, Elsevier, AmsterdamGoogle Scholar
  9. Brock W, Durlauf S (2001) Growth empirics and reality. World Bank Econ Rev 15:229–272CrossRefGoogle Scholar
  10. Caselli F (2005) Accounting for cross-country income differences. In: Aghion P, Durlauf S (eds) Handbook of economic growth, vol 1. North Holland, Elsevier, AmsterdamGoogle Scholar
  11. Caves D, Christensen L, Diewert W (1982a) The economic theory of index numbers and measurement of input, output and productivity. Econometrica 50:1393–1414CrossRefGoogle Scholar
  12. Caves D, Christensen L, Diewert W (1982b) Multilateral comparison of output, input and productivity using superlative index numbers. Econ J 92:73–86CrossRefGoogle Scholar
  13. Ciccone A, Jarocinski M (2010) Determinants of economic growth: will data tell? Am Econ J: Macroecon 4:222–246Google Scholar
  14. Coelli T, Rao D, Battese G (1998) An introduction to efficiency and productivity analysis. Kluwer, BostonCrossRefGoogle Scholar
  15. Collins S, Bosworth B (1996) Economic growth in East Asia: accumulation versus assimilation. Brookings Pap Econ Act 2:135–203CrossRefGoogle Scholar
  16. Daraio C, Simar L (2007) Advanced robust and nonparametric methods in efficiency analysis: methodology and applications. Springer, New YorkGoogle Scholar
  17. De Long B, Summers L (1991) Equipment investment and economic growth. Q J Econ 106:445–502CrossRefGoogle Scholar
  18. Durlauf S, Kourtellos A, Tan C (2008) Are any growth theories robust? Econ J 118:329–346CrossRefGoogle Scholar
  19. Easterly W (1993) How much do distortions affect growth? J Monet Econ 32:187–212CrossRefGoogle Scholar
  20. Easterly W, Levine R (2001) It’s not factor accumulation: stylized facts and growth models. World Bank Econ Rev 15:177–219CrossRefGoogle Scholar
  21. Eaton J, Kortum S (2001) Technology, trade, and growth: a unified framework. Eur Econ Rev 45:742–755CrossRefGoogle Scholar
  22. Eicher T, Papageorgiou C, Raftery A (2011) Default priors and predictive performance in Bayesian Model Averaging, with application to growth determinants. J Appl Econom 26:30–55CrossRefGoogle Scholar
  23. Färe R, Grosskopf S, Norris S, Zhang Z (1994) Productivity growth, technical progress, and efficiency change in industrialized countries. Am Econ Rev 84:66–83Google Scholar
  24. Fernandez C, Ley E, Steel M (2001) Model uncertainty in cross-country growth regressions. J Appl Econom 16:563–576CrossRefGoogle Scholar
  25. Gallup J, Mellinger A, Sachs J (1999) Geography datasets. Center for International Development at Harvard University (CID)Google Scholar
  26. George E (1999) Discussion of Bayesian model averaging and model search strategies. In: Bernardo J, Berger A, Dawid P (eds) Bayesian statistics. Oxford University Press, OxfordGoogle Scholar
  27. Grossman G, Helpman E (1991) Innovation and growth in the global economy. MIT Press, CambridgeGoogle Scholar
  28. Hall R, Jones C (1999) Why do some countries produce so much more output per worker than others? Q J Econ 114:83–116CrossRefGoogle Scholar
  29. Helpman E, Rangel A (1999) Adjusting to a new technology: experience and training. J Econ Growth 4:359–383CrossRefGoogle Scholar
  30. Isaksson A (2007) Determinants of total factor productivity: a literature review. Research and Statistics Staff Working Paper 2/2007, United Nations Industrial Development Organization, ViennaGoogle Scholar
  31. Klenow P, Rodriguez-Clare A (1997) The neoclassical revival in growth economics: has it gone too far? NBER Macroeconomics Annual 12:73–102Google Scholar
  32. Kneller R, Stevens P (2006) Frontier technology and absorptive capacity: evidence from OECD manufacturing industries? Oxf Bull Econ Stat 68:1–21CrossRefGoogle Scholar
  33. Koop G (2003) Bayesian econometrics. Wiley, New YorkGoogle Scholar
  34. Koop G, Osiewalski J, Steel M (1999) The components of output growth: a stochastic frontier analysis. Oxf Bull Econ Stat 61:455–487CrossRefGoogle Scholar
  35. Kraay A, Tawara N (2010) Can disaggregated indicators identify governance reform priorities? World Bank Policy Research Working Paper 5254Google Scholar
  36. Krüger J (2003) The global trends of total factor productivity: evidence from the nonparametric Malmquist index approach. Oxf Econ Pap 55:265–286CrossRefGoogle Scholar
  37. Krugman P (1994) The age of diminishing expectations: US Economic Policy in the 1990s. MIT Press, CambridgeGoogle Scholar
  38. Kumar S, Russell R (2002) Technological change, technological catch-up, and capital deepening: relative contributions to growth and convergence. Am Econ Rev 92:527–548CrossRefGoogle Scholar
  39. Makiela K (2009) Economic growth decompositon. An empirical analysis using Bayesian frontier approach. Cent Eur J Econ Model Econom 1:333–369Google Scholar
  40. Malmquist S (1953) Index numbers and indifference surfaces. Trabajos de Estadistica y de Investigacion Operativa 4:209–242CrossRefGoogle Scholar
  41. Miller S, Upadhyay M (2000) The effects of openness, trade orientation, and human capital on total factor productivity. J Dev Econ 63:399–423CrossRefGoogle Scholar
  42. Moral-Benito E (2011) Model averaging in economics. Working Paper Bank of Spain 1123Google Scholar
  43. Moral-Benito E (2012) Determinants of economic growth: a Bayesian panel data approach. Rev Econ Stat 94:566–579CrossRefGoogle Scholar
  44. Nehru V, Dhareshwar A (1993) A new database on physical capital stock: sources, methodology and results. Revista de Analisis Economico 8:37–59Google Scholar
  45. Pesaran H (2004) General diagnostic tests for cross section dependence in panels. Cambridge Working Papers in Economics 0435, University of CambridgeGoogle Scholar
  46. Porter M, Stern S (2000) Measuring the ‘ideas’ production function: evidence from international patent output. NBER Working Paper, 7891Google Scholar
  47. Raftery A (1995) Bayesian model selection in social research. Sociol Methodol 25:111–163CrossRefGoogle Scholar
  48. Romer P (1990) Endogenous technological change. J Polit Econ 98:71–102CrossRefGoogle Scholar
  49. Sala-i-Martin X, Doppelhofer G, Miller R (2004) Determinants of long-term growth: a Bayesian averaging of classical estimates (BACE) approach. Am Econ Rev 94:813–835CrossRefGoogle Scholar
  50. Simar L (2003) Detecting outliers in frontier models: a simple approach. J Prod Anal 20:391–424CrossRefGoogle Scholar
  51. Solow R (1957) Technical change and the aggregate production function. Rev Econ Stat 39:312–320CrossRefGoogle Scholar
  52. Suhariyanto K, Thirtle C (2001) Asian agricultural productivity and convergence. J Agric Econ 52:96–110CrossRefGoogle Scholar
  53. Valdes B (1999) Economic growth: theory, empirics and policy. Edward Elgar, Gloucester and NorthamptonGoogle Scholar
  54. Vandenbussche J, Aghion P, Meghir C (2006) Growth, distance to frontier and composition of human capital. J Econ Growth 11:97–127CrossRefGoogle Scholar
  55. Wallich H (1969) Money and growth: a country cross-section analysis. J Money Credit Bank 1:281–302CrossRefGoogle Scholar
  56. Wooldridge J (2002) Econometric analysis of cross section and panel data. MIT Press, CambridgeGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Michael Danquah
    • 1
  • Enrique Moral-Benito
    • 2
  • Bazoumana Ouattara
    • 3
  1. 1.GIMPAAccraGhana
  2. 2.Banco de EspañaMadridSpain
  3. 3.Swansea UniversitySwanseaUK

Personalised recommendations